arXiv AI June 2, 2026 By Ritabrata Chakraborty, Shivakumara Palaiahnakote, Umapada Pal, Cheng-Lin Liu

A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition

arXiv:2503. 15639v2 Announce Type: replace-cross Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 22

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...

By Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele

llms diffusion computer-vision fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 25

Arbitrarily Shaped Scene Text Detection: A Decade of Advances and Systematic Analysis

arXiv:2107.11800v2 Announce Type: replace Abstract: Scene text detection has become an important research area in computer vision. However, dynamic changes in scenes and the complex diversity of text...

By Pengwen Dai, Feiyang He, Chaolang Li, Xugong Qin, Wenqi Ren, Xiaochun Cao

computer-vision benchmarks

Hugging Face Trending Papers

Jun 23

Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods

WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general Scene Text Recognition (STR). Existing STR datasets and methods, typically built around regular scene text and fixed-template inputs, struggle to scale to WATER.

llms computer-vision multimodal benchmarks

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 25

MEVL-STP: Multi-Encoder and Vision Language Model for Arbitrarily Shaped Scene Text Spotting

MEVL-STP introduces a two‑stage pipeline for spotting arbitrarily shaped scene text. The detection stage fuses features from six frozen vision encoders via a hierarchical Feature Pyramid Network and a Progressive Scale Expansion network to produce precise polygon masks. The recognition stage then crops these masks and feeds them to a fine‑tuned Qwen3‑VL‑8B‑Instruct model, achieving state‑of‑the‑art detection and end‑to‑end performance on CTW1500, Total‑Text, and ICDAR 2015 without synthetic pretraining.

By Aman Anand, Partha Pratim Roy, Shivakumara Palaiahnakote

llms computer-vision fine-tuning multimodal

arXiv AI

Jul 31

TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions

arXiv:2607. 26107v1 Announce Type: cross Abstract: Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions.

By Xinran Liu, Shouqian Shi, Yutong Chen, Ge Wang, Xin-Wei Yao, Sheng Zhong

rag computer-vision multimodal benchmarks safety

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Related stories

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

Arbitrarily Shaped Scene Text Detection: A Decade of Advances and Systematic Analysis

Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

MEVL-STP: Multi-Encoder and Vision Language Model for Arbitrarily Shaped Scene Text Spotting

TraceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions